Spectral representation of the annual cycle in the climate change signal

摘要

The annual cycle of temperature and precipitation changes as projected byclimate models is of fundamental interest in climate impact studies. Itsestimation, however, is impaired by natural variability. Using a simple formof the delta change method, we show that on regional scales relevant forhydrological impact models, the projected changes in the annual cycle areprone to sampling artefacts. For precipitation at station locations, theseartefacts may have amplitudes that are comparable to the climate changesignal itself. Therefore, the annual cycle of the climate change signalshould be filtered when generating climate change scenarios. We test aspectral smoothing method to remove the artificial fluctuations. Comparisonagainst moving monthly averages shows that sampling artefacts in the climatechange signal can successfully be removed by spectral smoothing. The methodis tested at Swiss climate stations and applied to regional climate modeloutput of the ENSEMBLES project. The spectral method performs well,except in cases with a strong annual cycle and large relative precipitationchanges.